Archive for the ‘Newsletters’ Category

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Control Groups in Small Populations

Friday, February 5th, 2010

The following is from the January 2010 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment and I’ll reply.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: Thank you for your recent article about Control Groups.  Our organization launched an online distance learning program this past August, and I’ve just completed some student behavior analysis for this past semester.

Using weekly RF-Scores based on Recently and Frequently they’ve logged in to courses within the previous three weeks, I’m able to assess their “Risk Level”– how likely they are to stop using the program.  We had a percentage who discontinued the program, but in retrospect, their login behavior and changes in their login behavior gave strong indication they were having trouble before they completely stopped using it.

A: Fantastic!  I have spoken with numerous online educators about this application of Recency – Frequency modeling, as well online research subscriptions, a similar behavioral model.  All reported great results predicting student / subscriber defection rates.

Q: I’m preparing to propose a program for the upcoming semester where we contact students by email and / or phone when their login behavior gives indication that they’re having trouble.  My hope is that by proactively contacting these students, we can resolve issues or provide assistance before things escalate to the point they defect completely.

A: Absolutely, the yield (% students / revenue retained) on a project like this should be excellent.  Plus, you will end up learning a lot about “why”, which will lead to better executions of the “potential dropout” program the more you test it.

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Choosing the Size of Control Groups

Tuesday, December 29th, 2009

The following is from the December 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment and I’ll reply.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

 Q:  I am a big fan of your web site and read your Drilling Down book. Great work!

A:  Thanks for the kind words!

Q:  I was wondering if you could help me picking the right control group size for a project of ours?  The population is 25 million telco customers that for which we want to do a long term impact analysis (month by month) in regards to revenue increase versus control group.  The marketing initiatives are mix of retention, lifecycle and tactical/seasonal activities.  We want to measure revenue increase through any of the marketing activities compared to control group.

A:   Great project, this is the kind of idea that can really improve margins if you can find out which specific tactics drop the most profit to the bottom line.

Q:   I have searched the web for some help and found calculators that say: On 25 million and smallest expected uplift of 0.1% and highest likely rate of > 5% the calculator gives 250k (1%).  Is that sufficient to calculate the net impact on the remaining base?  Would be very grateful if you could give me your thoughts.

A:  Well, it could be and might not be…

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Posted in DataBase Marketing, Newsletters, Web Analytics | 1 Comment »

Customer Value in the Freemium Model

Friday, December 4th, 2009

The following is from the November 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment and I’ll reply.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: You kindly clarified a few issues when I was reading Drilling Down earlier this year – so I hope you don’t mind the direct email.

A: Yes, I remember!

I am working for www.XYZ.com, a social networking / virtual world site based abroad but visitors are 85% US.

Our growth up to now has been mainly viral and in the summer we hit 1.2M UVs operating on the Freemium model with only 5% of our registered users converting to paying customers and a significant portion of our revenue coming from ads.  On average our customers are active on the site for something like 4 months making their first purchase around day 28. 

But to take us to the next stage we are embarking on some marketing for the first time using AdWords and various revenue share campaigns, and of course to do this sensibly we need to arrive at a reasonable estimate of LTV.

A: Makes sense!

Q: To calculate an adjusted LTV I removed all customers with a lifetime of less than 4 months but this gives a low estimate as this calculation ignores the bumper summer months and the extra paid for features put in place earlier this year.  Calculating LTV using ARPU and monthly churn (not sure how to calculate this in our environment) gives another different estimate.  Is there any help or advice you could perhaps give us?  If not in the US then perhaps you could recommend somebody abroad – can’t find anything in the literature relevant for start-up like us.

A:  It sounds to me like you’re trying to make this too complicated, at least for the place you are at this time.  Monthly churn and the “28 day” threshold are nice to know on a tactical level, but LTV is more of a Strategic idea that does not necessarily benefit from analysis at that level.  And you may not really want LTV, but a derivative that might be more helpful.

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Member Retention in Professional Orgs

Wednesday, November 4th, 2009

The following is from the October 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment and I’ll reply.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: I have recently purchased your book Drilling Down and going through the many interesting concepts.

A: Thanks for that!

Q:  I work for a membership Organization and we would like to conduct some analysis into who we may lose and approach them even before their membership lapses.  But the only problem here is that we carry data only on the purchases made (though many of our members do not purchase our products and stay a member) and web site visits.

A:  Are you *sure* that’s all the data you collect?  I once worked with a professional membership org that thought they only had one data source, but turns out they had 8 – from 8 different areas of the org – that nobody really knew about.

Q:  How do I know if a particular member is going to resign and lapse soon with this limited amount of behavioral data.  Recently it’s been a concern that we are losing members who have been with us for more than 10 years and who are in their mid career profession (aged between 30 to 45) and indicated no specific reason for resignation. 

This has been going on for the last few months and now we would like to strategically target these customers and approach them even before they react negative.  What concepts could help me to do this? Your guidance would be much appreciated.

A:  OK, my answer will be in two sections: if you (hopefully) find you have more data than you think, and if you really don’t have any other data to fall back on.

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Posted in DataBase Marketing, Marketing thru Operations, Newsletters | No Comments »

Relational vs. Transactional

Friday, October 2nd, 2009

The following is from the September 2009 Drilling Down Newsletter (original title:  Customer Retention for Restaurants).  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment.

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q:  I am hoping you can help answer a question for our team.  By way of introduction, I am the CEO of XXXX.  We are a specialty retailer / restaurant of gourmet pizza, salads and sandwiches.  We would like to know  restaurant industry averages (pizza industry if possible) for customer retention – What percentage of customers that have ordered once from a particular restaurant order from them a second time?  I am hoping with your years of expertise and harnessing data you may be able to assist us with this question.  Look forward to hearing from you.

A:  Unfortunately, in those said years of experience, I have found little hard information on customer retention rates in QSR and restaurants in general (if anyone has data, please leave in Comments).  It’s just the nature of the business that little hard data, if collected, is stored in such a way that one can aggregate at the customer level.  The high percentage of cash transactions doesn’t help matters much; there’s a lot of data missing.

Over the years, sometimes you see data leak out for tests of loyalty programs, and of course clients sometimes have anecdotal or survey data, but this is not much help in getting to a “true” retention rate.  More often than not you discover serious biases in the way the data was collected so at best, you have a biased view of a narrow segment.  Often what you get is a notion of retention among best customers, or customers willing to sign up for a loyalty card, but not all customers.  And the large “middle” group of customers is where all the Marketing leverage is.

What to do about this predicament?  

There are really two issues in your question; the idea of using industry benchmarks when analyzing customer performance, and the measurement of retention in restaurants.

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Posted in Analytical Culture, Customer Experience, DataBase Marketing, Marketing thru Operations, Measuring Engagement, Newsletters | 1 Comment »

RFM versus LifeCycle Grids

Friday, August 28th, 2009

The following is from the August 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q:  First of all, thank you for the excellent book!  I’m really excited about digging into our own customer data to see what we’ll learn.

A:  Thank you for the kind words!

Q:  However, when you’re creating the RF Scores, what is the standard timeframe you should use?  I have access to about 5 years worth of purchase data – should I create RF scores based on the last 5 years, 3 years, 2 years, 6 months?

Our sales are quite cyclical, so I think the baseline should probably be at least a year, and I’m considering doing two years.  It seems as though if I get too much larger than that, my results will be too watered down. 

I’m also planning on generating “historical” RF scores by filtering my data to reflect the purchases only up to a certain point.  So, to generate a Q1-09 score, I’d create it from sales data of Q1-07 through Q1-09.  The Q2-09 score would be from Q2-07 through Q2-09, etc.  Does this make sense?  It will allow us to see the changes that have been happening in our company even though we’re only just now looking at the data.  It will give me a picture of what it would have looked like, had I looked at it back then.

A:  I think you have accurately understood the situation and have the right approach!  This type of analysis is very sensitive to time frame.

There are really 2 broad types of customer analysis.  There is analysis for action in the present, a Tactical approach driving towards a “we should do this now” result, and the more Strategic analysis, which is informational and says “this is what we should have done then” and / or “this is why we should make these business changes”.  The shorter time frame is Tactical, the longer timeframe Strategic.

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Posted in DataBase Marketing, Measuring Engagement, Newsletters | 1 Comment »

Loyalty Program Structure & Tracking

Friday, July 31st, 2009

The following is from the July 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q:  I’m involved in a loyalty program analytics project.  This client is a local pharmacy.  All sales are done directly in store, the web site is just for communication purposes.  The general problem we are trying to solve is the manager doesn’t have any detailed ideas about shoppers behavior apart from human observation. 

The idea is to launch a card-based loyalty program which will track sales activity and give insight into customer behavior.  The program will be points-based calculated on amount spent.  Points can be redeemed as rebates, coupons, gift certificates, or use points to buy items in loyalty program catalog.

The task is to segment customers according to their recent purchase behavior and determine the customer lifecycle.  I’ve been able to do some basic analysis using the R package and MySQL database, but am unable to detect customer lifecycle. 

Can you please give me guidance on this?

A:  What is the Objective of detecting the LifeCycle, to create a more “active” customer retention program?  Loyalty programs can be quite “passive” and often benefit from a more active overlay.  But there can be many reasons to want to understand the LifeCycle…

Q:  My 2nd task is to use the behavioral data with demographics to  build a direct marketing strategy and provide management with insight into the customer base, for example: percent new customers, % of Gold customers who passed to Silver in last quarter.

A:  Again, it would be helpful to understand how management would take action on this data.  But I suppose you are in the common position of not knowing the tactical approach, and nobody will lay it out for you (a.k.a. they are clueless)…and you don’t know the right questions to ask or how to ask them.

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Lead Scoring and Nurturing

Friday, July 3rd, 2009

The following Q & A is from the June 2009 Drilling Down Newsletter.

Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, Feel free to leave a comment.  Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q: I received this article (Norms of Reciprocity, measuring value of Social Marketing) via a friend’s Twitter account.  Very interesting.

A:  Glad you enjoyed it!

Q:  It has made open up my ACT! database, and my Outlook databases and add the metric of Growing / Strong / Weakening / Failed to my normal Sales and Business progress metrics.  If I group those categories and correlate to traditional metrics, it’s impressive how they reflect each other.

A:  Yes, most people are surprised.  It’s a very, very simple idea that seems to work across just about any human activity including crime, attendance, and so forth.  

The more Recently someone has done something, the more likely they are to do it again.  Conversely, the longer since an activity last took place, the less likely the person will do it again.  Often called Recency in Psychology and studied quite a bit.

Q:  Now I have to think about how I really use and apply this. : )

A:  Well, if I can guess you are in Sales from your title, typically one of the best applications is in what Strategic Marketing folks might call “allocation of resources”, which probably translates into “lead nurturing” for you.

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Posted in DataBase Marketing, Measuring Engagement, Newsletters | 2 Comments »

Hacking the RFM Model

Friday, May 29th, 2009

The following is from the May 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q:  First of all thank you for your help.  I have some questions I would be pleased if you answer them for me.

A:  No problem!

Q:  1. RFM analysis – is it possible to use some other ranking technique rather than quintiles? Using quintiles for bigger databases will cause many tied values, isn’t it a problem?

A:  Sure, you can use it any way it works best for you.  There is no “magic” behind quintiles, you can use deciles or whatever works best. It’s the idea of ranking by Recency, Frequency, and Value that is the key concept in the model.

I’ve seen dozens and perhaps hundreds of variations on the core RFM model, depending on how you classify a “variation”.  One change that’s common is changing the scaling, as you mention above, to accommodate the size of the database.  Smaller databases use quartiles or even tertiles.  Larger databases, choose the ordered distribution that meets the need.

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Posted in Customer Experience, DataBase Marketing, Measuring Engagement, Newsletters, Web Analytics | 1 Comment »

Measuring Social Media Value

Friday, May 1st, 2009

The following is from the April 2009 Drilling Down Newsletter.  Got a question about Customer Measurement, Management, Valuation, Retention, Loyalty, Defection?  Just ask your question.  Also, feel free to leave a comment. 

Want to see the answers to previous questions?  Here’s the blog archive; the pre-blog newsletter archives are here.

Q:  I’m a social media consultant, facing the interesting challenges of measuring success, and wondered, what are your thoughts on social media measurement and life time value? The two seem to go together, but if anyone has thought about it, you would have.  Would love to know your thoughts.

A:  Just to be clear, the following is specifically about social for use as a Marketing platform, not as a utility or a way to keep in touch with people.  Interacting with other people can create a lot of value – emotional value for the participants.  There are obviously lots of great uses for social platforms and I’m sure there is more to come in that area.  The question is: does any of this make sense as “media”?

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Posted in Customer Experience, Newsletters, Web Analytics | 2 Comments »

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